Day-Ahead Hourly Solar Photovoltaic Output Forecasting Using SARIMAX, Long Short-Term Memory, and Extreme Gradient Boosting: Case of the Philippines

Ian B. Benitez, Jessa A. Ibañez, Cenon III D. Lumabad, Jayson M. Cañete and Jeark A. Principe ()
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Ian B. Benitez: National Engineering Center, University of the Philippines Diliman, Quezon City 1101, Philippines
Jessa A. Ibañez: National Engineering Center, University of the Philippines Diliman, Quezon City 1101, Philippines
Cenon III D. Lumabad: National Engineering Center, University of the Philippines Diliman, Quezon City 1101, Philippines
Jayson M. Cañete: National Engineering Center, University of the Philippines Diliman, Quezon City 1101, Philippines
Jeark A. Principe: Department of Geodetic Engineering, University of the Philippines Diliman, Quezon City 1101, Philippines

Energies, 2023, vol. 16, issue 23, 1-21

Abstract: This study explores the forecasting accuracy of SARIMAX, LSTM, and XGBoost models in predicting solar PV output using one-year data from three solar PV installations in the Philippines. The research aims to compare the performance of these models with their hybrid counterparts and investigate their performance. The study utilizes the adjusted shortwave radiation (SWR) product in the Advanced Himawari Imager 8 (AHI-8), as a proxy for in situ solar irradiance, and weather parameters, to improve the accuracy of the forecasting models. The results show that SARIMAX outperforms LSTM, XGBoost, and their combinations for Plants 1 and 2, while XGBoost performs best for Plant 3. Contrary to previous studies, the hybrid models did not provide more accurate forecasts than the individual methods. The performance of the models varied depending on the forecasted month and installation site. Using adjusted SWR and other weather parameters, as inputs in forecasting solar PV output, adds novelty to this research. Future research should consider comparing the accuracy of using adjusted SWR alone and combined with other weather parameters. This study contributes to solar PV output forecasting by utilizing adjusted satellite-derived solar radiation, and combining SARIMAX, LSTM, and XGBoost models, including their hybrid counterparts, in a single and comprehensive analysis.

Keywords: LSTM; SARIMAX; solar PV output forecasting; XGBoost (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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